legex/inference.py

"""LLM inference over Goldenset full_text or per-country PDFs.

For each registered country, reads case inputs from either:
- the `full_text` column of `data/<cc>/Goldenset_*.xlsx`, or
- the PDFs at `data/<cc>/*.pdf`

runs an LLM with the Vorlage coding rules, and appends a CSV of predictions
to `data/<cc>/Goldenset_{country}_{prompt_version}_{full_text|pdf}_{model}.csv`.
Successful rows (by case_id) are skipped; rows with a non-empty `error`
column are retried on the next run.
"""

import argparse
import asyncio
import csv
import json
import logging
import os
import sys
import threading
import time
from collections.abc import Callable
from datetime import date
from pathlib import Path

from dotenv import load_dotenv
from openpyxl import load_workbook

from legex.config import settings
from legex.models.base import Case
from legex.models.classification import Classification
from legex.prompts import PromptPlan, load_plan
from legex.scrapers import SCRAPERS
from legex.utils import (
    classified_csv_path,
    countries_with_goldenset,
    goldenset_path,
    goldenset_sheet,
    load_coding_rules,
    load_goldenset_columns,
    load_isic_categories,
    norm_case_id,
    pdf_paths,
    read_full_text_jsonl,
)

log = logging.getLogger(__name__)

_DATE_FIELD_KEYS: frozenset[str] = frozenset(
    name
    for name, info in Classification.model_fields.items()
    if date in getattr(info.annotation, "__args__", (info.annotation,))
)


def _coerce_json_dates(data: dict) -> None:
    """JSON has no date type; convert strings before pydantic validation."""
    for key in _DATE_FIELD_KEYS:
        if key not in data:
            continue
        val = data[key]
        if isinstance(val, str) and val:
            data[key] = date.fromisoformat(val)


def _parse_classification(content: str) -> Classification:
    """Parse LLM JSON; accept a one-element array when the model returns a list."""
    data = json.loads(content)
    if isinstance(data, list):
        if len(data) != 1 or not isinstance(data[0], dict):
            raise ValueError("expected a JSON object or a one-element array of objects")
        data = data[0]
    _coerce_json_dates(data)
    return Classification.model_validate(data)


class _RequestRateLimiter:
    """Minimum spacing between LLM calls (60 / rpm seconds). None rpm = no limit."""

    def __init__(self, rpm: int | None) -> None:
        self._min_interval = 60.0 / rpm if rpm else 0.0
        self._last_at = 0.0
        self._sync_lock = threading.Lock()
        self._async_lock: asyncio.Lock | None = None

    def acquire(self) -> None:
        if self._min_interval <= 0:
            return
        with self._sync_lock:
            now = time.monotonic()
            wait = self._min_interval - (now - self._last_at)
            if wait > 0:
                time.sleep(wait)
            self._last_at = time.monotonic()

    async def acquire_async(self) -> None:
        if self._min_interval <= 0:
            return
        if self._async_lock is None:
            self._async_lock = asyncio.Lock()
        async with self._async_lock:
            now = time.monotonic()
            wait = self._min_interval - (now - self._last_at)
            if wait > 0:
                await asyncio.sleep(wait)
            self._last_at = time.monotonic()


def inference_output_path(cc: str, prompt_version: str, source: str, model: str) -> Path:
    return classified_csv_path(cc, prompt_version, source, model)


# CSV columns (and GOLDENSET headers) use the alias when one is set,
# e.g. `Currency_dispute_value_nominal` (capital C). The Python attribute
# is the lowercase model field name. These maps move between the two.
_FIELD_TO_COL: dict[str, str] = {
    name: (info.alias or name) for name, info in Classification.model_fields.items()
}
_COL_TO_FIELD: dict[str, str] = {col: field for field, col in _FIELD_TO_COL.items()}


def _output_columns() -> list[str]:
    headers = load_goldenset_columns(settings.template)
    return [h for h in headers if h != "full_text"] + ["model", "error"]


def _read_goldenset_cases(cc: str) -> list[Case]:
    """Pull case_id / link / full_text rows from the GOLDENSET sheet.

    Falls back to data/<cc>/full_text.jsonl (keyed by case_id) when the xlsx
    full_text column is empty or absent, so jurisdictions whose text doesn't
    fit in Excel can still be classified.
    """
    path = goldenset_path(cc)
    wb = load_workbook(path, read_only=True, data_only=True)
    ws = goldenset_sheet(wb)
    rows = ws.iter_rows(values_only=True)
    header = [str(c) if c is not None else "" for c in next(rows)]
    idx = {h.strip().lower(): i for i, h in enumerate(header) if h}
    for required in ("case_id", "link"):
        if required not in idx:
            raise ValueError(f"{path} GOLDENSET sheet missing column {required!r}")
    full_text_idx = idx.get("full_text")
    fallback = read_full_text_jsonl(cc)
    if full_text_idx is None and not fallback:
        raise ValueError(
            f"{path} GOLDENSET sheet has no full_text column and no "
            f"data/{cc}/full_text.jsonl fallback"
        )

    cases: list[Case] = []
    n_from_jsonl = 0
    for row in rows:
        if not any(row):
            continue
        case_id = row[idx["case_id"]]
        link = row[idx["link"]]
        full_text = row[full_text_idx] if full_text_idx is not None else None
        if not full_text and case_id is not None:
            cid_s = str(case_id)
            fb = fallback.get(cid_s) or fallback.get(norm_case_id(cid_s))
            if fb:
                full_text = fb
                n_from_jsonl += 1
        cases.append(
            Case(
                case_id=str(case_id) if case_id is not None else None,
                link=str(link) if link is not None else None,
                jurisdiction=cc,
                full_text=str(full_text) if full_text is not None else None,
            )
        )
    if n_from_jsonl:
        log.info(f"[{cc}] using full_text from full_text.jsonl for {n_from_jsonl} case(s)")
    return cases


def _read_pdf_cases(cc: str) -> list[Case]:
    """One Case per PDF at data/<cc>/*.pdf; case_id = file stem."""
    from pypdf import PdfReader

    cases: list[Case] = []
    for pdf in pdf_paths(cc):
        try:
            reader = PdfReader(str(pdf))
            text = "\n".join((page.extract_text() or "") for page in reader.pages)
        except Exception as e:
            log.warning(f"[{cc}] failed to read {pdf.name}: {type(e).__name__}: {e}")
            text = ""
        cases.append(
            Case(
                case_id=pdf.stem,
                link=str(pdf),
                jurisdiction=cc,
                full_text=text or None,
            )
        )
    return cases


def _read_cases(cc: str, source: str) -> list[Case]:
    if source == "full_text":
        return _read_goldenset_cases(cc)
    return _read_pdf_cases(cc)


def _empty_row(case: Case, model: str, columns: list[str]) -> dict[str, str]:
    row: dict[str, str] = {col: "" for col in columns}
    row["case_id"] = case.case_id or ""
    row["link"] = case.link or ""
    row["model"] = model
    return row


async def _classify_case_single(
    case: Case,
    system_prompt: str,
    model: str,
    columns: list[str],
    limiter: _RequestRateLimiter,
) -> dict[str, str]:
    import litellm

    row = _empty_row(case, model, columns)
    if not case.full_text:
        row["error"] = "no full_text"
        return row

    try:
        await limiter.acquire_async()
        resp = await litellm.acompletion(
            model=model,
            messages=[
                {"role": "system", "content": system_prompt},
                {"role": "user", "content": case.full_text},
            ],
            response_format=Classification,
        )
        content = resp["choices"][0]["message"]["content"]
        parsed = _parse_classification(content)
    except Exception as e:
        row["error"] = f"{type(e).__name__}: {e}"
        return row

    extras = [col for col in _FIELD_TO_COL.values() if col not in row]
    if extras:
        row["error"] = f"Classification fields not in GOLDENSET header: {extras}"
        return row
    for field, col in _FIELD_TO_COL.items():
        value = getattr(parsed, field)
        row[col] = "" if value is None else str(value)
    return row


async def _classify_one_column(
    column: str,
    system_prompt: str,
    full_text: str,
    model: str,
    limiter: _RequestRateLimiter,
) -> tuple[str, object | None, str | None]:
    """Return (CSV column, value, error). `column` is the CSV header name
    (alias-aware); we read the corresponding Python attribute off the
    parsed Classification."""
    import litellm

    try:
        await limiter.acquire_async()
        resp = await litellm.acompletion(
            model=model,
            messages=[
                {"role": "system", "content": system_prompt},
                {"role": "user", "content": full_text},
            ],
            response_format={"type": "json_object"},
        )
        content = resp["choices"][0]["message"]["content"]
        parsed = _parse_classification(content)
        field = _COL_TO_FIELD.get(column, column)
        return column, getattr(parsed, field), None
    except Exception as e:
        return column, None, f"{column}: {type(e).__name__}: {e}"


async def _classify_case_per_column(
    case: Case,
    column_systems: dict[str, str],
    model: str,
    columns: list[str],
    limiter: _RequestRateLimiter,
) -> dict[str, str]:
    row = _empty_row(case, model, columns)
    if not case.full_text:
        row["error"] = "no full_text"
        return row

    extras = [c for c in column_systems if c not in row]
    if extras:
        row["error"] = f"Per-column prompts target fields not in GOLDENSET header: {extras}"
        return row

    results = await asyncio.gather(
        *(
            _classify_one_column(col, sys_prompt, case.full_text, model, limiter)
            for col, sys_prompt in column_systems.items()
        )
    )
    errors: list[str] = []
    for col, value, err in results:
        if err is not None:
            errors.append(err)
        elif value is not None:
            row[col] = str(value)
    if errors:
        row["error"] = "; ".join(errors)
    return row


class _CsvRowWriter:
    """Append rows to a CSV, flushing after each write."""

    def __init__(self, path: Path, columns: list[str]) -> None:
        path.parent.mkdir(parents=True, exist_ok=True)
        write_header = not path.exists()
        self._file = open(path, "a", encoding="utf-8", newline="")
        self._writer = csv.DictWriter(self._file, fieldnames=columns)
        if write_header:
            self._writer.writeheader()

    def write_row(self, row: dict[str, str]) -> None:
        self._writer.writerow(row)
        self._file.flush()

    def close(self) -> None:
        self._file.close()

    def __enter__(self) -> "_CsvRowWriter":
        return self

    def __exit__(self, *args: object) -> None:
        self.close()


def _classify_cases(
    cases: list[Case],
    plan: PromptPlan,
    model: str,
    columns: list[str],
    limiter: _RequestRateLimiter,
    write_row: Callable[[dict[str, str]], None],
    concurrency: int = 1,
) -> int:
    """Classify cases and stream each result via write_row. Returns success count.

    With concurrency > 1, up to N cases are processed in parallel; the rate
    limiter still gates total request throughput. Rows are written as each
    case finishes, so output order is non-deterministic when concurrency > 1.
    """

    async def run_all() -> int:
        sem = asyncio.Semaphore(max(1, concurrency))

        async def process(case: Case) -> dict[str, str]:
            async with sem:
                if plan.mode == "single":
                    return await _classify_case_single(
                        case, plan.system or "", model, columns, limiter
                    )
                return await _classify_case_per_column(
                    case, plan.column_systems or {}, model, columns, limiter
                )

        ok = 0
        for coro in asyncio.as_completed([process(c) for c in cases]):
            row = await coro
            write_row(row)
            if not row["error"]:
                ok += 1
        return ok

    return asyncio.run(run_all())


def _successful_case_ids(path: Path) -> set[str]:
    if not path.exists():
        return set()
    ids: set[str] = set()
    with open(path, encoding="utf-8", newline="") as f:
        for r in csv.DictReader(f):
            if (r.get("error") or "").strip():
                continue
            cid = (r.get("case_id") or "").strip()
            if cid:
                ids.add(cid)
    return ids


def _drop_case_ids(path: Path, columns: list[str], case_ids: set[str]) -> int:
    """Remove rows for the given case_ids. Returns number of rows removed."""
    if not path.exists() or not case_ids:
        return 0
    with open(path, encoding="utf-8", newline="") as f:
        rows = list(csv.DictReader(f))
    kept = [r for r in rows if (r.get("case_id") or "").strip() not in case_ids]
    removed = len(rows) - len(kept)
    if removed == 0:
        return 0
    with open(path, "w", encoding="utf-8", newline="") as f:
        writer = csv.DictWriter(f, fieldnames=columns, extrasaction="ignore")
        writer.writeheader()
        writer.writerows(kept)
    return removed


def classify(
    countries: list[str] | None,
    model: str,
    source: str,
    prompt_version: str,
    limit: int | None,
    rpm: int | None = None,
    concurrency: int = 1,
) -> None:
    rules = load_coding_rules(settings.template)
    isic = load_isic_categories(settings.template)
    columns = _output_columns()
    plan = load_plan(prompt_version, rules, isic)
    limiter = _RequestRateLimiter(rpm)
    if rpm:
        log.info(f"Rate limit: {rpm} requests/min ({limiter._min_interval:.2f}s between calls)")
    if concurrency > 1:
        log.info(f"Concurrency: up to {concurrency} cases in parallel")

    targets = countries or countries_with_goldenset() or list(SCRAPERS)
    for cc in targets:
        if source == "full_text":
            gs = goldenset_path(cc)
            if not gs.exists():
                log.warning(f"[{cc}] missing {gs}, skipping")
                continue
        else:
            if not pdf_paths(cc):
                log.warning(f"[{cc}] no PDFs at {settings.data_dir / cc}/*.pdf, skipping")
                continue

        cases = _read_cases(cc, source)
        if not cases:
            log.info(f"[{cc}] no cases found, skipping")
            continue

        out = inference_output_path(cc, prompt_version, source, model)
        done = _successful_case_ids(out)
        todo = [c for c in cases if (c.case_id or "") not in done]
        if limit is not None:
            todo = todo[:limit]
        if not todo:
            log.info(f"[{cc}] all {len(cases)} cases already classified in {out}, skipping")
            continue

        retry_ids = {c.case_id for c in todo if c.case_id}
        n_removed = _drop_case_ids(out, columns, retry_ids)
        n_new = len(todo) - n_removed
        if n_removed:
            log.info(f"[{cc}] retrying {n_removed} failed case(s), {n_new} new")

        log.info(
            f"[{cc}] classifying {len(todo)} cases ({len(done)} already done) "
            f"in {plan.mode} mode → {out}"
        )
        with _CsvRowWriter(out, columns) as writer:
            n_ok = _classify_cases(
                todo, plan, model, columns, limiter, writer.write_row, concurrency
            )
        log.info(f"[{cc}] classified {n_ok}/{len(todo)} → {out}")


def main() -> None:
    logging.basicConfig(
        level=logging.INFO,
        format="%(asctime)s [%(levelname)s] %(message)s",
        handlers=[logging.StreamHandler(sys.stderr)],
    )
    load_dotenv(override=True)
    print(f"gemini key {os.environ.get('GEMINI_API_KEY')}")  # sanity check for env var

    parser = argparse.ArgumentParser(
        prog="legex-classify",
        description="Run LLM inference over Goldenset full_text or per-country PDFs.",
    )
    parser.add_argument(
        "--country",
        action="extend",
        nargs="+",
        dest="countries",
        help="Country code (repeatable). Defaults to all registered scrapers.",
    )
    parser.add_argument(
        "--model",
        default="gpt-4o-mini",
        help="litellm model id (e.g. gpt-4o-mini, anthropic/claude-opus-4-7).",
    )
    parser.add_argument(
        "--prompt_version",
        default="v1",
        help="Prompt version under legex/prompts/ (default: v1).",
    )
    parser.add_argument(
        "--limit",
        type=int,
        default=None,
        help="Cap NEW cases per country (useful for cheap dry runs).",
    )
    parser.add_argument(
        "--rpm",
        type=int,
        default=None,
        metavar="N",
        help="Max LLM requests per minute (applies to every completion call).",
    )
    parser.add_argument(
        "--concurrency",
        type=int,
        default=1,
        metavar="N",
        help="Process up to N cases in parallel (default: 1).",
    )
    source = parser.add_mutually_exclusive_group(required=True)
    source.add_argument(
        "--full_text",
        dest="source",
        action="store_const",
        const="full_text",
        help="Read input from the full_text column of data/<cc>/Goldenset_*.xlsx.",
    )
    source.add_argument(
        "--pdf",
        dest="source",
        action="store_const",
        const="pdf",
        help="Read input from data/<cc>/*.pdf.",
    )
    args = parser.parse_args()
    if args.rpm is not None and args.rpm <= 0:
        parser.error("--rpm must be a positive integer")
    if args.concurrency <= 0:
        parser.error("--concurrency must be a positive integer")

    classify(
        countries=args.countries,
        model=args.model,
        source=args.source,
        prompt_version=args.prompt_version,
        limit=args.limit,
        rpm=args.rpm,
        concurrency=args.concurrency,
    )


if __name__ == "__main__":
    main()